In functional brain imaging a series of brain images is acquired. The time elapsed between each acquisition is usually a
few seconds or less. Due to the small acquisition times required, these images
usually have poor resolution. Furthermore, as the imaging parameters
are tuned to highlight physiological changes (e.g. blood oxygenation) the
images often have poor anatomical contrast.
Extracting functional information from such a series of images is done
by applying statistical time-series analysis, which assumes that the
location of a given voxel within the brain does not change over
time. However, there is usually some degree of subject motion within
the scanner, especially when the scanning takes a long time or when
clinical patients are involved. Therefore, in order to render the
data fit for statistical analysis this motion must be estimated and
corrected for. This is the task of motion correction methods and it
is essentially a multiple-image registration task.
Normally motion correction methods deal with the registration task by
selecting a reference image from within the series and registering
each image in turn to this fixed reference. As all images are of the
same subject, using the same imaging parameters, it can be classified
an intra-subject, intra-modal registration problem. Therefore, a
rigid-body transformation space and intra-modal cost function can be
used. Furthermore, as the values in the corrected images are
important for later statistical analysis, the choice of interpolation
method for the transformation of the images is of particular
importance (Hajnal et al.,1995a, b).
Next:Methods Up:Materials Previous:Multi-Resolution Techniques
Peter Bannister
2002-05-03